Advances in technology have increased the ability of consumers to monitor their electricity consumption. Electricity sensors are now widely available as consumer electronics, for monitoring the total electricity consumption of a household. Most of the sensors are capable of transmitting sensory data to a cloud service for potential analysis or other usage, but they typically have a data capture rate of 1 Hz or lower. Each sample of data captured may include parameters such as voltage, current, apparent power, reactive power and energy for each individual phase. Most homes have two and some have three phases. When a connection is reliable, the captured data is transmitted evenly, but when the connection is intermittent, the data can be transmitted in batches.
FIG. 1 shows a graph of electricity power consumption based on data captured at a rate of 1 Hz. The x-axis 10 is a measure of time in seconds (t/s) and the y-axis 12 is a measure of power in watts (P/W). The portion 14 of the graph is level, and may correspond to some lights being on, or a router or PVR, for example. At some time between point 16 at 10 seconds and the subsequently captured data point 18 at 11 seconds, another electricity consuming device is switched on and the graph rises linearly 20 due to interpolation between points 16 and 18. The device that is switched on between points 16 and 18 may be a fridge that is cycling to keep cool, a dryer, a chandelier or any other electric or electronic device. After the additional device is on, portion 22 of the graph shows some small fluctuations, which may be due to noise or to changes in the power consumed by the devices that are on. Temperature changes in devices may cause their resistive loads to change during operation, for example. Power, when drawn, may not always fluctuate, though. It could be more noisy if certain devices are on (e.g. TV, computer) but often when the activity at home is limited or not present, the power is fairly flat. The power graph may be obtained from a sensing device that reports the average value of power, current, voltage, etc. every second, as well as the amount of energy that is used over any period of time. While the graph in FIG. 1 is shown as a power graph, it can be safely assumed that a current graph with a 1 Hz resolution would look the same, since the voltage graph would normally be near, or as good as, constant.
Existing systems may be optimized to report data only after the detection of events of interest, such as a turn-on or a turn-off of an electricity consuming device. Also, when a user logs into the app or portal that connects to, and forms part of, the system, the electricity monitoring device may start bursting data at a 1 Hz rate so that users can see immediate responses to their actions. When they log out or leave, the system goes back to event-based transmission.
Some of the sensors in production are capable of sensing AC voltage and current signals with a very high frequency, up to 16.5 KHz in some cases. Besides the monitoring of basic electricity consumption, the sensory data, particularly the high frequency data, may be used for more advanced purposes such as individual load monitoring and user behavior monitoring.
FIG. 2 shows graphs of voltage 30 and current 32 on a common, millisecond time axis 34, which spans one cycle of a 60 Hz electricity supply. Raw data has been recorded at a rate of 16.5 kHz. As it can be seen, there is much more information in this current signal than in the previous power graph. Point 36 may correspond to a switch on of a device, and point 38 may correspond to a switch off of a device, and there may or may not be a device already switched on. Alternately, the rise in current at point 36 and the drop in current at point 38 may be associated with the AC nature of the supply. Nevertheless, the detail of the current graph includes localized maxima 40 and minima 42, which may be indicative of a specific electric or electronic device that is switched on. Detail in the current graph or any other graphs related to power consumption or appliance usage may individually or collectively represent a fingerprint or signature of an appliance. Smaller fluctuations 44 superimposed on a smoothly varying portion of the current graph may simply be noise.
Despite the ability to capture data at higher rates, the low frequency data capture rate has become mainstream due to bandwidth limitations and because of the redundancy existing within high frequency data. For a comparison, sensing data at 1 Hz and transmitting it to a server may require 50 MB/day, taking into account network overhead, whereas sensing and transmitting at 15 kHz would require a bandwidth of 750 GB/day. Even if the higher resolution data were compressed, it would still require, say, 7 GB/day. The bandwidth needed is therefore at least two orders of magnitude less than the bandwidth at which the raw data is received from the sensor.
This background information is provided to reveal information believed by the applicant to be of possible relevance to the present invention. No admission is necessarily intended, nor should be construed, that any of the preceding information constitutes prior art against the present invention, unless explicitly specified.